BERT_without_preprocessing_grid_search – AI Model by LovenOO | AlphaNeural AI | AlphaNeural AI

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LovenOO/
BERT_without_preprocessing_grid_search

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text-classification

generated_from_trainer

google-bert/bert-base-uncased

autotrain_compatible

endpoints_compatible

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BERT_without_preprocessing_grid_search

This model is a fine-tuned version of bert-base-uncased on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.6213
Precision: 0.8399
Recall: 0.8622
F1: 0.8498
Accuracy: 0.8798

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 2e-05
train_batch_size: 32
eval_batch_size: 32
seed: 42
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 10

Training results

Training Loss	Epoch	Step	Validation Loss	Precision	Recall	F1	Accuracy
No log	1.0	257	0.6305	0.7254	0.8018	0.7512	0.8180
0.8689	2.0	514	0.4877	0.8120	0.8500	0.8245	0.8667
0.8689	3.0	771	0.4490	0.7911	0.8590	0.8148	0.8599
0.2702	4.0	1028	0.4748	0.8291	0.8689	0.8457	0.8730
0.2702	5.0	1285	0.5217	0.8326	0.8543	0.8413	0.8783
0.1505	6.0	1542	0.5288	0.8351	0.8650	0.8481	0.8754
0.1505	7.0	1799	0.5801	0.8417	0.8585	0.8487	0.8769
0.092	8.0	2056	0.5721	0.8402	0.8694	0.8535	0.8818
0.092	9.0	2313	0.6135	0.8453	0.8618	0.8522	0.8808
0.0723	10.0	2570	0.6213	0.8399	0.8622	0.8498	0.8798

Framework versions

Transformers 4.31.0
Pytorch 2.0.1+cu118
Datasets 2.14.4
Tokenizers 0.13.3